An archaeology of female monasticism in medieval Ireland

This thesis considers the archaeological evidence for female monasticism in medieval Ireland, with a particular emphasis on the later medieval period. Female monasticism has been considered from an archaeological perspective in several countries, most notably Britain, but has yet to be considered in any detail in Ireland. The study aims to bring together all the currently available evidence on female monasticism and consider it through an engendered archaeological approach. The data gathering for this research has been deliberately wide, and where gaps have been identified in the Irish evidence, comparative material from elsewhere has been considered. Nunneries should not be expected to conform to what has become the male monastic template of a claustrally-planned monastery. The research conducted shows a distinct and varied archaeology and architecture for medieval nunneries in Ireland which suggests that a claustral plan was not considered an essential part of a nunnery scheme. Nunneries provided an enclosed environment where women, for a variety of motives could become brides of Christ. Through the performance and celebration of the daily Divine Office, the Mass and seasonal liturgy, spaces used by the nunnery community were negotiated and transformed into a sacred Paradise on earth. However, rather than being isolated in the landscape nunneries in later medieval Ireland were located either within or close to walled towns, larger unenclosed settlements and settlement clusters and would have been well known throughout their hinterlands. This research concludes that nunneries were an intrinsic part of the medieval monastic landscape in Ireland and an essential component of patrons’ portfolios of patronage, at a particularly local level, and where they interacted closely with their local community.

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.